rnorm(10) # draw 10 random values from a Standard Normal distribution [1] 2.1893126 -0.3920248 0.6233079 -0.3044554 1.0206581 -0.8628790
[7] 0.9408118 1.1756534 0.6488329 -0.9160198
Mainstream in academia for statistical computing and data science, increasingly used in business. Job market advantage!
Free & open-source: wherever you go, R will be with you at no costs (unlike MATLAB, MPLUS, SPSS, etc.)
Real programming language: difficult at the beginning, but: 1) gives you lots of flexibility; 2) has transfer on other programming languages (e.g., Python).
Vast community support thanks to a large and active community (plus chatGPT, Gemini, Lucrez-IA, etc., know it pretty well!).
Huge ecosystem, >23,000 packages on CRAN, more from other sources (e.g., GitHub), to do amazing stuff with statistical data analysis, machine learning, data visualization, developing webapps [shiny], writing reports and even entire books [bookdown, rmarkdown]); also, can integrate with Quarto, GitHub.
Facilitates reproducible scientific research by sharing code and workflows.
Executing fundamental operations and using basic functions;
Working with essential data types and structures;
Gaining some proficiency in managing and manipulating data with vectors and dataframes;
Understanding some fundamental concepts of programming.
Core statistical inference methods;
LM/LMM/GLMM: (Generalized) linear (mixed-effects) models;
Data visualization using ggplot2;
Power analysis & more via data simulation;
SEM: Structural Equation Modeling;
Conducting meta-analysis.
see Shiny gallery
here’s a couple of recent real examples from Psicostat members:
this game-like shiny app developed for the science4all event in Padova; see here some explanation in Italian
practical ad-hoc shiny app for scoring experimental data collected by students
examples of other resources that can be created within the R ecosystem, integrating other tools such as GitHub and Quarto:
this very course support material is a website in its own right
this very course textbook is a book/website
this book by Daniël Lakens explaining Statistical Inference
Make sure you install:
Interesting alternatives to installing RStudio:
Let’s run a few commands in RStudio to familiarize with its console and see if the installation works properly
[1] 2.1893126 -0.3920248 0.6233079 -0.3044554 1.0206581 -0.8628790
[7] 0.9408118 1.1756534 0.6488329 -0.9160198
[1] 89 89 75 104 84 59 111 92 94 77